package com.handy.util.gifencoder;
//NeuQuant.java源码（处理GIF图片） 
 
/* NeuQuant Neural-Net Quantization Algorithm
 * ------------------------------------------
 *
 * Copyright (c) 1994 Anthony Dekker
 *
 * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
 * See "Kohonen neural networks for optimal colour quantization"
 * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
 * for a discussion of the algorithm.
 *
 * Any party obtaining a copy of these files from the author, directly or
 * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
 * world-wide, paid up, royalty-free, nonexclusive right and license to deal
 * in this software and documentation files (the "Software"), including without
 * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
 * and/or sell copies of the Software, and to permit persons who receive
 * copies from any such party to do so, with the only requirement being
 * that this copyright notice remain intact.
 */

// Ported to Java 12/00 K Weiner

public class NeuQuant {

 protected static final int netsize = 256; /* number of colours used */

 /* four primes near 500 - assume no image has a length so large */
 /* that it is divisible by all four primes */
 protected static final int prime1 = 499;
 protected static final int prime2 = 491;
 protected static final int prime3 = 487;
 protected static final int prime4 = 503;

 protected static final int minpicturebytes = (3 * prime4);
 /* minimum size for input image */

 /* Program Skeleton
    ----------------
    [select samplefac in range 1..30]
    [read image from input file]
    pic = (unsigned char*) malloc(3*width*height);
    initnet(pic,3*width*height,samplefac);
    learn();
    unbiasnet();
    [write output image header, using writecolourmap(f)]
    inxbuild();
    write output image using inxsearch(b,g,r)      */

 /* Network Definitions
    ------------------- */

 protected static final int maxnetpos = (netsize - 1);
 protected static final int netbiasshift = 4; /* bias for colour values */
 protected static final int ncycles = 100; /* no. of learning cycles */

 /* defs for freq and bias */
 protected static final int intbiasshift = 16; /* bias for fractions */
 protected static final int intbias = (((int) 1) << intbiasshift);
 protected static final int gammashift = 10; /* gamma = 1024 */
 protected static final int gamma = (((int) 1) << gammashift);
 protected static final int betashift = 10;
 protected static final int beta = (intbias >> betashift); /* beta = 1/1024 */
 protected static final int betagamma =
  (intbias << (gammashift - betashift));

 /* defs for decreasing radius factor */
 protected static final int initrad = (netsize >> 3); /* for 256 cols, radius starts */
 protected static final int radiusbiasshift = 6; /* at 32.0 biased by 6 bits */
 protected static final int radiusbias = (((int) 1) << radiusbiasshift);
 protected static final int initradius = (initrad * radiusbias); /* and decreases by a */
 protected static final int radiusdec = 30; /* factor of 1/30 each cycle */

 /* defs for decreasing alpha factor */
 protected static final int alphabiasshift = 10; /* alpha starts at 1.0 */
 protected static final int initalpha = (((int) 1) << alphabiasshift);

 protected int alphadec; /* biased by 10 bits */

 /* radbias and alpharadbias used for radpower calculation */
 protected static final int radbiasshift = 8;
 protected static final int radbias = (((int) 1) << radbiasshift);
 protected static final int alpharadbshift = (alphabiasshift + radbiasshift);
 protected static final int alpharadbias = (((int) 1) << alpharadbshift);

 /* Types and Global Variables
 -------------------------- */

 protected byte[] thepicture; /* the input image itself */
 protected int lengthcount; /* lengthcount = H*W*3 */

 protected int samplefac; /* sampling factor 1..30 */

 //   typedef int pixel[4];                /* BGRc */
 protected int[][] network; /* the network itself - [netsize][4] */

 protected int[] netindex = new int[256];
 /* for network lookup - really 256 */

 protected int[] bias = new int[netsize];
 /* bias and freq arrays for learning */
 protected int[] freq = new int[netsize];
 protected int[] radpower = new int[initrad];
 /* radpower for precomputation */

 /* Initialise network in range (0,0,0) to (255,255,255) and set parameters
    ----------------------------------------------------------------------- */
 public NeuQuant(byte[] thepic, int len, int sample) {

  int i;
  int[] p;

  thepicture = thepic;
  lengthcount = len;
  samplefac = sample;

  network = new int[netsize][];
  for (i = 0; i < netsize; i++) {
   network[i] = new int[4];
   p = network[i];
   p[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;
   freq[i] = intbias / netsize; /* 1/netsize */
   bias[i] = 0;
  }
 }
 
 public byte[] colorMap() {
  byte[] map = new byte[3 * netsize];
  int[] index = new int[netsize];
  for (int i = 0; i < netsize; i++)
   index[network[i][3]] = i;
  int k = 0;
  for (int i = 0; i < netsize; i++) {
   int j = index[i];
   map[k++] = (byte) (network[j][0]);
   map[k++] = (byte) (network[j][1]);
   map[k++] = (byte) (network[j][2]);
  }
  return map;
 }
 
 /* Insertion sort of network and building of netindex[0..255] (to do after unbias)
    ------------------------------------------------------------------------------- */
 public void inxbuild() {

  int i, j, smallpos, smallval;
  int[] p;
  int[] q;
  int previouscol, startpos;

  previouscol = 0;
  startpos = 0;
  for (i = 0; i < netsize; i++) {
   p = network[i];
   smallpos = i;
   smallval = p[1]; /* index on g */
   /* find smallest in i..netsize-1 */
   for (j = i + 1; j < netsize; j++) {
    q = network[j];
    if (q[1] < smallval) { /* index on g */
     smallpos = j;
     smallval = q[1]; /* index on g */
    }
   }
   q = network[smallpos];
   /* swap p (i) and q (smallpos) entries */
   if (i != smallpos) {
    j = q[0];
    q[0] = p[0];
    p[0] = j;
    j = q[1];
    q[1] = p[1];
    p[1] = j;
    j = q[2];
    q[2] = p[2];
    p[2] = j;
    j = q[3];
    q[3] = p[3];
    p[3] = j;
   }
   /* smallval entry is now in position i */
   if (smallval != previouscol) {
    netindex[previouscol] = (startpos + i) >> 1;
    for (j = previouscol + 1; j < smallval; j++)
     netindex[j] = i;
    previouscol = smallval;
    startpos = i;
   }
  }
  netindex[previouscol] = (startpos + maxnetpos) >> 1;
  for (j = previouscol + 1; j < 256; j++)
   netindex[j] = maxnetpos; /* really 256 */
 }
 
 /* Main Learning Loop
    ------------------ */
 public void learn() {

  int i, j, b, g, r;
  int radius, rad, alpha, step, delta, samplepixels;
  byte[] p;
  int pix, lim;

  if (lengthcount < minpicturebytes)
   samplefac = 1;
  alphadec = 30 + ((samplefac - 1) / 3);
  p = thepicture;
  pix = 0;
  lim = lengthcount;
  samplepixels = lengthcount / (3 * samplefac);
  delta = samplepixels / ncycles;
  alpha = initalpha;
  radius = initradius;

  rad = radius >> radiusbiasshift;
  if (rad <= 1)
   rad = 0;
  for (i = 0; i < rad; i++)
   radpower[i] =
    alpha * (((rad * rad - i * i) * radbias) / (rad * rad));

  //fprintf(stderr,"beginning 1D learning: initial radius=%d\n", rad);

  if (lengthcount < minpicturebytes)
   step = 3;
  else if ((lengthcount % prime1) != 0)
   step = 3 * prime1;
  else {
   if ((lengthcount % prime2) != 0)
    step = 3 * prime2;
   else {
    if ((lengthcount % prime3) != 0)
     step = 3 * prime3;
    else
     step = 3 * prime4;
   }
  }

  i = 0;
  while (i < samplepixels) {
   b = (p[pix + 0] & 0xff) << netbiasshift;
   g = (p[pix + 1] & 0xff) << netbiasshift;
   r = (p[pix + 2] & 0xff) << netbiasshift;
   j = contest(b, g, r);

   altersingle(alpha, j, b, g, r);
   if (rad != 0)
    alterneigh(rad, j, b, g, r); /* alter neighbours */

   pix += step;
   if (pix >= lim)
    pix -= lengthcount;

   i++;
   if (delta == 0)
    delta = 1;
   if (i % delta == 0) {
    alpha -= alpha / alphadec;
    radius -= radius / radiusdec;
    rad = radius >> radiusbiasshift;
    if (rad <= 1)
     rad = 0;
    for (j = 0; j < rad; j++)
     radpower[j] =
      alpha * (((rad * rad - j * j) * radbias) / (rad * rad));
   }
  }
  //fprintf(stderr,"finished 1D learning: final alpha=%f !\n",((float)alpha)/initalpha);
 }
 
 /* Search for BGR values 0..255 (after net is unbiased) and return colour index
    ---------------------------------------------------------------------------- */
 public int map(int b, int g, int r) {

  int i, j, dist, a, bestd;
  int[] p;
  int best;

  bestd = 1000; /* biggest possible dist is 256*3 */
  best = -1;
  i = netindex[g]; /* index on g */
  j = i - 1; /* start at netindex[g] and work outwards */

  while ((i < netsize) || (j >= 0)) {
   if (i < netsize) {
    p = network[i];
    dist = p[1] - g; /* inx key */
    if (dist >= bestd)
     i = netsize; /* stop iter */
    else {
     i++;
     if (dist < 0)
      dist = -dist;
     a = p[0] - b;
     if (a < 0)
      a = -a;
     dist += a;
     if (dist < bestd) {
      a = p[2] - r;
      if (a < 0)
       a = -a;
      dist += a;
      if (dist < bestd) {
       bestd = dist;
       best = p[3];
      }
     }
    }
   }
   if (j >= 0) {
    p = network[j];
    dist = g - p[1]; /* inx key - reverse dif */
    if (dist >= bestd)
     j = -1; /* stop iter */
    else {
     j--;
     if (dist < 0)
      dist = -dist;
     a = p[0] - b;
     if (a < 0)
      a = -a;
     dist += a;
     if (dist < bestd) {
      a = p[2] - r;
      if (a < 0)
       a = -a;
      dist += a;
      if (dist < bestd) {
       bestd = dist;
       best = p[3];
      }
     }
    }
   }
  }
  return (best);
 }
 public byte[] process() {
  learn();
  unbiasnet();
  inxbuild();
  return colorMap();
 }
 
 /* Unbias network to give byte values 0..255 and record position i to prepare for sort
    ----------------------------------------------------------------------------------- */
 public void unbiasnet() {

  int i;

  for (i = 0; i < netsize; i++) {
   network[i][0] >>= netbiasshift;
   network[i][1] >>= netbiasshift;
   network[i][2] >>= netbiasshift;
   network[i][3] = i; /* record colour no */
  }
 }
 
 /* Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in radpower[|i-j|]
    --------------------------------------------------------------------------------- */
 protected void alterneigh(int rad, int i, int b, int g, int r) {

  int j, k, lo, hi, a, m;
  int[] p;

  lo = i - rad;
  if (lo < -1)
   lo = -1;
  hi = i + rad;
  if (hi > netsize)
   hi = netsize;

  j = i + 1;
  k = i - 1;
  m = 1;
  while ((j < hi) || (k > lo)) {
   a = radpower[m++];
   if (j < hi) {
    p = network[j++];
    try {
     p[0] -= (a * (p[0] - b)) / alpharadbias;
     p[1] -= (a * (p[1] - g)) / alpharadbias;
     p[2] -= (a * (p[2] - r)) / alpharadbias;
    } catch (Exception e) {
    } // prevents 1.3 miscompilation
   }
   if (k > lo) {
    p = network[k--];
    try {
     p[0] -= (a * (p[0] - b)) / alpharadbias;
     p[1] -= (a * (p[1] - g)) / alpharadbias;
     p[2] -= (a * (p[2] - r)) / alpharadbias;
    } catch (Exception e) {
    }
   }
  }
 }
 
 /* Move neuron i towards biased (b,g,r) by factor alpha
    ---------------------------------------------------- */
 protected void altersingle(int alpha, int i, int b, int g, int r) {

  /* alter hit neuron */
  int[] n = network[i];
  n[0] -= (alpha * (n[0] - b)) / initalpha;
  n[1] -= (alpha * (n[1] - g)) / initalpha;
  n[2] -= (alpha * (n[2] - r)) / initalpha;
 }
 
 /* Search for biased BGR values
    ---------------------------- */
 protected int contest(int b, int g, int r) {

  /* finds closest neuron (min dist) and updates freq */
  /* finds best neuron (min dist-bias) and returns position */
  /* for frequently chosen neurons, freq[i] is high and bias[i] is negative */
  /* bias[i] = gamma*((1/netsize)-freq[i]) */

  int i, dist, a, biasdist, betafreq;
  int bestpos, bestbiaspos, bestd, bestbiasd;
  int[] n;

  bestd = ~(((int) 1) << 31);
  bestbiasd = bestd;
  bestpos = -1;
  bestbiaspos = bestpos;

  for (i = 0; i < netsize; i++) {
   n = network[i];
   dist = n[0] - b;
   if (dist < 0)
    dist = -dist;
   a = n[1] - g;
   if (a < 0)
    a = -a;
   dist += a;
   a = n[2] - r;
   if (a < 0)
    a = -a;
   dist += a;
   if (dist < bestd) {
    bestd = dist;
    bestpos = i;
   }
   biasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));
   if (biasdist < bestbiasd) {
    bestbiasd = biasdist;
    bestbiaspos = i;
   }
   betafreq = (freq[i] >> betashift);
   freq[i] -= betafreq;
   bias[i] += (betafreq << gammashift);
  }
  freq[bestpos] += beta;
  bias[bestpos] -= betagamma;
  return (bestbiaspos);
 }
}
 
